Cover TM, Thomas JA. (1991). Elements of Information Theory.
Fletcher R. (1987). Practical methods of optimization (2nd ed).
Freund Y, Schapire RE. (1999). Large margin classification using the perceptron algorithm Mach Learn. 37
Freund Y, Schapire RE, Singer Y, Iyer R. (2003). An efficient boosting algorithm for combining preferences J Mach Learn Res. 4
Harrington EF. (2003). Online ranking-collaborative filtering using the perceptron algorithm Proceedings of the Twentieth International Conference on Machine Learning.
Kemeny JG, Snell JL. (1962). Mathematical models in the social sciences.
Kivinen J, Warmuth MK. (1997). Exponentiated gradient versus gradient descent for linear predictors Information And Computation. 132
Levin A, Shashua A. (2002). Ranking with large margin principle: Two approaches Advances in neural information processing systems. 15
Littlestone N. (1987). Learning when irrelevant attributes abound 28th Annual Symposium on Foundations of Computer Science.
Littlestone N. (1988). Learning when irrelevant attributes abound: A new linear-threshold algorithm Mach Learn. 2
Littlestone N. (1989). Mistake bounds and logarithmic linear-threshold learning algorithms Unpublished doctoral dissertation.
Mcjones P. (1997). Eachmovie collaborative filtering data set Available online at: http:--wwwfiresearch.digital.com-SRC-eachmovie-.
Obermayer K, Graepel T, Herbrich R. (2000). Large margin rank boundaries for ordinal regression Advances in large margin classifiers.
ROSENBLATT F. (1958). The perceptron: a probabilistic model for information storage and organization in the brain. Psychological review. 65 [PubMed]
Schapire RE, Singer Y, Cohen W. (1999). Learning to order things J Art Intell Res. 10
Shawe-taylor J, Cristianini N. (2000). An introduction to support vector machines.
Singer Y, Crammer K. (2001). Pranking with ranking Advances in neural information processing systems. 14
Singer Y, Crammer K. (2001). Ultraconservative online algorithms for multiclass problems Proceedings of the Fourteenth Annual Conference on Computational Learning Theory.
Vapnik V. (1998). Statistical Learning Theory.
Widrow B, Hoff M. (1960). Adaptive switching circuits Western Electronic Show And Convention Reco. 4